Lei Wang

Bio: Lei Wang is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Portfolio & Portfolio optimization. The author has an hindex of 2, co-authored 3 publications receiving 11 citations.

A cost-effective approach to portfolio construction with range-based risk measures

Abstract: In this paper, we introduce a new class of risk measures and the corresponding risk minimizing portfolio optimization problem. Instead of measuring the expected deviation of a daily return from a s...

Financial Thought Experiment: A GAN-based Approach to Vast Robust Portfolio Selection

Abstract: Modern day trading practice resembles a thought experiment, where investors imagine various possibilities of future stock market and invest accordingly. Generative adversarial network (GAN) is highly relevant to this trading practice in two ways. First, GAN generates synthetic data by a neural network that is technically indistinguishable from the reality, which guarantees the reasonableness of the experiment. Second, GAN generates multitudes of fake data, which implements half of the experiment. In this paper, we present a new architecture of GAN and adapt it to portfolio risk minimization problem by adding a regression network to GAN (implementing the second half of the experiment). The new architecture is termed GANr. Battling against two distinctive networks: discriminator and regressor, GANr’s generator aims to simulate a stock market that is close to the reality while allow for all possible scenarios. The resulting portfolio resembles a robust portfolio with data-driven ambiguity. Our empirical studies show that GANr portfolio is more resilient to bleak financial scenarios than CLSGAN and LASSO portfolios.

Financial Thought Experiment: A GAN-based Approach to Vast Robust Portfolio Selection

Abstract: Modern day trading practice resembles a thought experiment, where investors imagine various possibilities of future stock market and invest accordingly. Generative adversarial network (GAN) is highly relevant to this trading practice in two ways. First, GAN generates synthetic data by a neural network that is technically indistinguishable from the reality, which guarantees the reasonableness of the experiment. Second, GAN generates multitudes of fake data, which implements half of the experiment. In this paper, we present a new architecture of GAN and adapt it to portfolio risk minimization problem by adding a regression network to GAN (implementing the second half of the experiment). The new architecture is termed GANr. Battling against two distinctive networks: discriminator and regressor, GANr’s generator aims to simulate a stock market that is close to the reality while allow for all possible scenarios. The resulting portfolio resembles a robust portfolio with data-driven ambiguity. Our empirical studies show that GANr portfolio is more resilient to bleak financial scenarios than CLSGAN and LASSO portfolios.

Portfolio Construction Based on the Modern Portfolio Theory Including Gold, Crude Oil and Cryptocurrency

Abstract: Finding and optimizing the best investment strategies is one of the most important skills in financial markets. However, the research on the construction of different types of investment portfolios including stocks, goods, and cryptocurrency is not perfect enough. This article constructs an optimal portfolio utilized Modern Portfolio Theory and Sharpe ratio. Based on the price data of two stocks in China Securities Index 300, three stocks in Standard and Poor's 500, Gold, Crude Oil and Bitcoin, the portfolio of 8 kinds of price data are simulated and calculated. A variety of optimal investment portfolios have been constructed, including the minimum risk investment portfolio and the highest Sharpe ratio investment portfolio. In addition, by setting the lower limit of the expected rate of return, the lowest-risk investment portfolio with customized expected rate of returns is obtained. Nevertheless, an attempt was made in the article to construct investment strategies for different types of investment projects. These results shed light on guiding further exploration of portfolio construction with different type of stock or goods.

Insurance: Mathematics and Economics

Abstract: This article has no abstract. Keywords: actuarial journals; insurance mathematics; insurance economics

Resolution of Degeneracy in Merton's Portfolio Problem

Abstract: The Merton problem determines the optimal intertemporal portfolio choice by maximizing the expected utility, and is the basis of modern portfolio theory in continuous-time finance. However, its empirical performance is disappointing. The estimation errors of the expected rates of returns make the optimal policy degenerate, resulting in an extremely low (or unbounded) expected utility value for a high-dimensional portfolio. We further prove that the estimation error of the variance-covariance matrix leads to the degenerated policy of solely investing in the risk-free asset. This study proposes a constrained l1 - minimization approach to resolve the degeneracy. The proposed scheme can be implemented with simple linear programming and involves negligible additional computational time, compared to standard estimation. We prove the consistency of our framework that our estimate of the optimal control tends to be the true one. We also derive the rate of convergence. Simulation studies are provided to verify the finite-sample properties. An empirical study using S&P 500 component stock data demonstrates the superiority of the proposed approach.